Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias
Published in MedRxiv, 2024
Biases inherent in electronic health records (EHRs), and therefore in medical artificial intelligence (AI) models may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature. Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases are further exacerbated, resulting in systems that perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs.
Recommended citation: Perets, Oriel, Emanuela Stagno, Eyal Ben Yehuda, Megan McNichol, Leo Anthony Celi, Nadav Rappoport, and Matilda Dorotic. "Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias." medRxiv (2024). https://www.medrxiv.org/content/10.1101/2024.04.09.24305594v1